Notes from Research in Progress: Data informed literacy

13 July, 2015

Title: Data informed learning: A next phase data literacy framework for higher education Researchers: Clarence Maybee (Purdue University), Lisa Zilinski (Carnegie Mellon University) and Jake Carlson (University of Michigan)
This work-in-progress aims to develop a data literacy framework for higher education that places learning about using data in the context of disciplinary learning. The development of data literacy may be informed by the half-century of scholarship aimed at framing information literacy to support learning in higher education. Typically grounded in the ACRL (2000) information literacy standards (Carlson, Fosmire, Miller, & Nelson, 2011; Prado & Marzal, 2013), our analysis has identified that emerging data literacy frameworks and curricula emphasize a skills-centric approach. Equally applicable to skills-centric constructions of data literacy, challenges have been made concerning the efficacy of generic approaches to information literacy, such as the standards, for enabling people to use information in the various contexts in which they live and work (e.g., Bruce, 1997; Lloyd, 2010). To inform an approach to data literacy that addresses contextual concerns, we explored information literacy frameworks that conceptualize using information as integral to learning within broader disciplinary or professional contexts. Informed learning (Bruce, 2008), which emphasizes learning as an outcome of engaging with information, was selected as an applicable framework from which to develop an approach to data literacy for use in higher education.

Drawing from informed learning (Bruce, 2008), our team has outlined guiding principles for data informed learning, which emphasize building on students’ prior experiences of using data, and having them engage with data while simultaneously learning about disciplinary content. For example, to build on students’ prior experiences of using data, the instructor in an accounting course may have students reflect on their own experiences of balancing a checkbook and then relate that to a journal ledger or a general ledger. A computer programming course may have students swap documented computer code with another team, and rerun a script to see if they can replicate the process, providing students with the opportunity to use and manage data in new ways while simultaneously learning about programming. A data literacy framework based on informed learning would support three important aspects of learning to use data in higher education learning contexts by: 1) guiding data-related instruction, 2) encouraging coursework relevancy by relating data use to subject-focused learning, and 3) supporting lifelong learning by enabling students to use data to learn in ways applicable to working in academic or professional environments. As with informed learning (Bruce, Somerville, Stoodley, & Partridge, 2013), the evolving construct of data informed learning needs to be further explored and developed through research. Such research would investigate the various experiences of using data in real-world environments, such as in business, social media, research labs, class projects, and so forth, to inform the design of learning environments where students use data in ways that support disciplinary learning outcomes.